The general flowchart for generating the input to the ML model can be seen in Fig. REF . Input features specify the geometry (topographic information of an area or indoor floorplan, position of antennas) and electromagnetic properties of a communication channel (pattern/polarization of antennas, permittivity/conductivity of various surfaces, frequency of operation). The input features have to be pre-processed before they are used by the ML model. That processing step may include feature scaling and normalization or dimensionality reduction techniques, among others [1]}. After processing the input data, input vectors \(x\)  are generated. Depending on how the target values \(y\)  are created, i.e via measurements or through a model, the input vectors may be different, hence the difference in notation (\(x_1\)  and \(x_2\) , respectively). Either way, measured or generated \(y\)  vectors will be used together with \(x_1\)  or \(x_2\)  to form the training pairs. After training, the ML model is able to output its prediction \(\hat{y}\)  for any new input vector \(x\) .
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